Strong and weak constraint variational assimilations for reduced order fluid flow modeling

Autores
Artana, Guillermo Osvaldo; Cammilleri, A.; Carlier, J.; Mémin, E.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system’s coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data.
Fil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cammilleri, A.. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Carlier, J.. No especifíca;
Fil: Mémin, E.. Institut National de Recherche en Informatique et en Automatique; Francia
Materia
PIV
POD
REDUCED ORDER DYNAMICAL SYSTEMS
VARIATIONAL ASSIMILATION
WAKE FLOW
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/195205
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/195205
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Strong and weak constraint variational assimilations for reduced order fluid flow modelingArtana, Guillermo OsvaldoCammilleri, A.Carlier, J.Mémin, E.PIVPODREDUCED ORDER DYNAMICAL SYSTEMSVARIATIONAL ASSIMILATIONWAKE FLOWhttps://purl.org/becyt/ford/2.3https://purl.org/becyt/ford/2In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system’s coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data.Fil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cammilleri, A.. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Carlier, J.. No especifíca;Fil: Mémin, E.. Institut National de Recherche en Informatique et en Automatique; FranciaAcademic Press Inc Elsevier Science2012-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/195205Artana, Guillermo Osvaldo; Cammilleri, A.; Carlier, J.; Mémin, E.; Strong and weak constraint variational assimilations for reduced order fluid flow modeling; Academic Press Inc Elsevier Science; Journal of Computational Physics; 231; 8; 4-2012; 3264-32880021-9991CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0021999112000319info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jcp.2012.01.010info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:10:53Zoai:ri.conicet.gov.ar:11336/195205instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 12:10:54.184CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Strong and weak constraint variational assimilations for reduced order fluid flow modeling
title Strong and weak constraint variational assimilations for reduced order fluid flow modeling
spellingShingle Strong and weak constraint variational assimilations for reduced order fluid flow modeling
Artana, Guillermo Osvaldo
PIV
POD
REDUCED ORDER DYNAMICAL SYSTEMS
VARIATIONAL ASSIMILATION
WAKE FLOW
title_short Strong and weak constraint variational assimilations for reduced order fluid flow modeling
title_full Strong and weak constraint variational assimilations for reduced order fluid flow modeling
title_fullStr Strong and weak constraint variational assimilations for reduced order fluid flow modeling
title_full_unstemmed Strong and weak constraint variational assimilations for reduced order fluid flow modeling
title_sort Strong and weak constraint variational assimilations for reduced order fluid flow modeling
dc.creator.none.fl_str_mv Artana, Guillermo Osvaldo
Cammilleri, A.
Carlier, J.
Mémin, E.
author Artana, Guillermo Osvaldo
author_facet Artana, Guillermo Osvaldo
Cammilleri, A.
Carlier, J.
Mémin, E.
author_role author
author2 Cammilleri, A.
Carlier, J.
Mémin, E.
author2_role author
author
author
dc.subject.none.fl_str_mv PIV
POD
REDUCED ORDER DYNAMICAL SYSTEMS
VARIATIONAL ASSIMILATION
WAKE FLOW
topic PIV
POD
REDUCED ORDER DYNAMICAL SYSTEMS
VARIATIONAL ASSIMILATION
WAKE FLOW
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.3
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system’s coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data.
Fil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cammilleri, A.. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Carlier, J.. No especifíca;
Fil: Mémin, E.. Institut National de Recherche en Informatique et en Automatique; Francia
description In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system’s coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data.
publishDate 2012
dc.date.none.fl_str_mv 2012-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/195205
Artana, Guillermo Osvaldo; Cammilleri, A.; Carlier, J.; Mémin, E.; Strong and weak constraint variational assimilations for reduced order fluid flow modeling; Academic Press Inc Elsevier Science; Journal of Computational Physics; 231; 8; 4-2012; 3264-3288
0021-9991
CONICET Digital
CONICET
url http://hdl.handle.net/11336/195205
identifier_str_mv Artana, Guillermo Osvaldo; Cammilleri, A.; Carlier, J.; Mémin, E.; Strong and weak constraint variational assimilations for reduced order fluid flow modeling; Academic Press Inc Elsevier Science; Journal of Computational Physics; 231; 8; 4-2012; 3264-3288
0021-9991
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0021999112000319
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jcp.2012.01.010
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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